Benchmarking methods integrating GWAS and single-cell transcriptomic data for mapping trait-cell type associations

Genome-wide association studies (GWAS) have discovered numerous trait-associated variants, but their biological context remains unclear. Integrating GWAS summary statistics with single-cell RNA-sequencing expression profiles can help identify the cell types in which these variants influence traits. Two main strategies have been developed to integrate these data types. The "single cell to GWAS" strategy (representing most methods) identifies gene sets with cell-type-specific expression and then follows with enrichment analyses applied to GWAS summary statistics. Conversely, the "GWAS to single cell" strategy begins with a list of trait-associated genes and calculates a cumulative disease score per cell based on gene expression count data. We systematically evaluated 19 approaches verses "ground truth" trait-cell type pairs to assess their statistical power and false positive rates. Based on these analyses, we draw seven key conclusions to guide future studies. We also propose a Cauchy approach to combine the two main strategies to maximize power for detecting trait-cell type associations.